The extraction of real-time velocity and noise-free detail from time-blurred frames of video has been inherently inaccurate. The problem is that current image detection technology is based upon raster, frame-at-a-time, or arrays of independent pixels for scene capture using light (x-ray, ultraviolet, infrared or other medium) integrating pixels. This process is limited in resolution by the number of pixels and their dimensions, and cannot avoid integrating noise into the detection process. Accordingly, attempts have been made to devise an improved image detection and processing mechanism.
For instance, J. C. Gillete (“Aliasing Reduction in Staring Infrared Imagers Utilizing SubPixel Techniques”) describes a method of uncontrolled micro-scanning for reducing aliased signal energy in a sequence of temporal image frames obtained by periodically sampling an image with a finite array of image detectors. Gillete takes a series of discrete low resolution samples of an image at a specified undersampling frequency, while spatially oscillating the detector between samples, thereby providing a sequence of static image frames, each having a subpixel offset relative to one another. By comparing the gray-scale values of successive image frames, for each image frame an estimate is calculated of each subpixel shift that occurs between successive image frames. Each image frame in the image sequence is then mapped onto a high resolution grid, based on the respective estimated interframe displacement. If the estimated shift is the same for multiple frames, then the pixel values at the overlapping positions are averaged to suppress noise.
Since Gillette only calculates an estimate of the subpixel shifts, Gillette is unable to determine the portion of the magnitude of the pixel values actually attributable to the subpixel shifts. This problem is compounded by the fact that Gillette averages the values of the pixels in successive frames that have the same estimated subpixel shift, thereby precluding removal of those aspects of the image frames not attributable to the subpixel shift. As such, the high resolution grid would include pixels whose values are not attributable to the subpixel shifts (eg. resulted from detector noise).
Further, Gillette must estimate the subpixel shift in each frame, resulting in multiple frame delays for one high-resolution image. Also, the frame basis of the method, and the corresponding finite exposure times, results in motion blur in each frame as objects traverse the scene. Additionally, given the discrete sampling nature of the method, aliasing in time is possible if the sampling frequency is insufficient for the scene motion.
H. Ogmen (“Neural Network Architectures for Motion Perception and Elementary Motion Detection in the Fly Visual System”) describes a neural network model of motion detection in the fly visual system. Ogmen uses center-surround opponency as the basis for both directional and non-directional motion detection, both in the center field-of-view and the periphery. However, Ogmen only performs statistical neural filtering post-processing of the vision data, thereby integrating noise with the vision data, with the ultimate result of reduced signal detection.
For the foregoing reasons, there is a need for an improved electronic imaging system.